3 papers
cs.AI2026
Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation
Seunghan Lee, Hyunsik Yoo, Jian Kang +2
Multi-behavior recommendation (MBR) leverages auxiliary behavioral signals, such as clicks and add-to-cart, to enhance target behavior prediction like purchases. While recent graph…
cs.IR2026
Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders
Dojun Hwang, Seunghan Lee, Cheonyoung Park +2
While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains challenging. Real-world items are d…
cs.IR2026
SCoRD: Semantic-Assisted Continual Retriever-Reranker Distillation for LLM-Based Recommendation
Seunghyun Baek, Gyuseok Lee, Seunghan Lee +3
Recommendation systems increasingly adopt a two-stage pipeline, where an ID-based retriever retrieves candidates and an LLM-based reranker refines their rankings. To improve retrie…